Big Book of R
1
Welcome :)
1.1
Your last-ever bookmark
1.2
Searching
1.3
Contributing
1.4
Contributors
1.5
Licence
1.6
Live stats
1.7
About me
2
New to R? Start here
2.0.1
Book: R for Data Science
2.0.2
Video Course: Getting started with R
3
Book Clubs
3.1
NHS-R community
3.2
R4DS Slack Community
3.3
R-ladies Netherlands - Advanced R by Hadley Wickham
4
Career & Community
4.1
Build Your Career in Data Science
4.2
Getting Started in Data Science
4.3
Twitter for R Programmers
4.4
Twitter for Scientists
4.5
Conversations On Data Science
4.6
Executive Data Science
4.7
Essays on Data Analysis
4.8
The Programmer’s Brain : What every programmer needs to know about cognition
5
Big Data
5.1
Exploring, Visualizing, and Modeling Big Data with R
5.2
Mastering Spark with R
6
Blogdown
6.1
blogdown: Creating Websites with R Markdown
7
Bookdown
7.1
bookdown: Authoring Books and Technical Documents with R Markdown
7.2
A Minimal Book Example
8
Data Science
8.1
R for Data Science
8.2
R for Data Science Solutions
8.3
Everyday Data Science
8.4
An Introduction to Data Analysis
8.5
Introduction to Data Science
8.6
Data Science: A First Introduction
8.7
Practical Data Science with R, Second Edition
8.8
R Programming for Data Science
8.9
Exploratory Data Analysis… by Roger D. Peng
8.10
edav.info/
8.11
APS 135: Introduction to Exploratory Data Analysis with R
8.12
The Art of Data Science
8.13
The Elements of Data Analytic Style
8.14
Beginning Data Science in R
8.15
Business Intelligence with R
8.16
R Data Science Quick Reference
8.17
Modern Data Science with R
8.18
Modern Statistics with R
8.19
Model-Based Clustering and Classification for Data Science
9
Data Visualization
9.1
ggplot2: Elegant Graphics for Data Analysis
9.2
A ggplot2 Tutorial for Beautiful Plotting in R
9.3
ggplot2 in 2
9.4
Data Visualization - A practical introduction
9.5
Data Processing & Visualization
9.6
Data Visualization in R
9.7
Data Visualization with R
9.8
R Graphics Cookbook, 2nd edition
9.9
plotly Interactive web-based data visualization with R, plotly, and shiny
9.10
Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code
9.11
BBC Visual and Data Journalism cookbook for R graphics
9.12
Fundamentals of Data Visualization
9.13
Graphical Data Analysis with R
9.14
JavaScript for R
10
Field specific
10.1
Analyzing Financial and Economic Data with R
10.2
Computer-age Calculus with R
10.3
Data Science in Education Using R
10.4
Data Skills for Reproducible Science
10.5
Learning Microeconometrics with R
10.6
Public Policy Analytics: Code & Context for Data Science in Government
10.7
Handbook of Regression Modeling in People Analytics
10.8
R Programming with Minecraft
10.9
Technical Foundations of Informatics
10.10
An introduction to quantitative analysis of political data in R
10.11
Machine Learning for Factor Investing
10.12
Introduction to Econometrics with R
10.13
How to be a modern scientist
10.14
Cryptocurrency Research: Open Source R Tutorial
11
Getting, cleaning and wrangling data
11.1
A Beginner’s Guide to Clean Data - beginners-guide-to-clean-data
11.2
21 Recipes for Mining Twitter Data with rtweet
11.3
Text Mining with R
11.4
Spreadsheet Munging Strategies
12
Geospatial
12.1
Geocomputation with R
12.2
Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny
12.3
Introduction to Spatial Data Programming with R
12.4
Spatial Data Science
12.5
Spatial Modelling for Data Scientists
12.6
Predictive Soil Mapping with R
12.7
Using R for Digital Soil Mapping
13
Journalism
13.1
Practical R for Mass Communication and Journalism
13.2
Using R for Data Journalism
14
Life Sciences
14.1
Assigning cell types with SingleR
14.2
Computational Genomics with R
14.3
Data Analysis for the Life Sciences
14.4
Git and Github for Advanced Ecological Data Analysis
14.5
Modern Statistics for Modern Biology
14.6
Orchestrating Single-Cell Analysis with Bioconductor
14.7
Statistics in R for Biodiversity Conservation Paperback
14.8
Numerical Ecology with R
14.9
Introduction to Data Analysis with R
15
Machine Learning
15.1
Hands-On Machine Learning with R
15.2
Feature Engineering and Selection: A Practical Approach for Predictive Models
15.3
Interpretable Machine Learning
15.4
Explanatory Model Analysis
15.5
Supervised Machine Learning for Text Analysis in R
15.6
Lightweight Machine Learning Classics with R
15.7
Machine Learning for Factor Investing
15.8
Mathematics and Programming for Machine Learning with R: From the Ground Up 1st Edition, Kindle
15.9
The caret Package
15.10
A Minimal rTorch Book
15.11
Model-Based Clustering and Classification for Data Science
15.12
Tidy Modeling with R
15.13
mlr3 book
16
Network analysis
16.1
Network Analysis in R Cookbook
17
Packages
17.1
The caret Package
17.2
ComplexHeatmap Complete Reference
17.3
data.table in R – The Complete Beginners Guide
17.4
GT Cookbook
17.5
Highcharter Cookbook
17.6
A Minimal rTorch Book
17.7
The Tidyverse Cookbook
17.8
The targets R Package User Manual
18
Reports: R Markdown and knitr
18.1
R Markdown: The Definitive Guide
18.2
R Markdown Cookbook
18.3
Getting used to R, RStudio, and R Markdown
18.4
Report Writing for Data Science in R
18.5
Introduction to R Markdown
18.6
RMarkdown for Scientists
18.7
Pimp my RMD: a few tips for R Markdown
18.8
knitr
18.9
Reproducible Research with R and RStudio
19
R package development
19.1
R packages
19.2
rOpenSci Packages: Development, Maintenance, and Peer Review
19.3
HTTP testing in R
20
R programming
20.1
Best Coding Practices for R
20.2
Modern R with the tidyverse
20.3
stats545 Data wrangling, exploration, and analysis with R
20.4
What They Forgot to Teach You About R
20.5
Field Guide to the R Ecosystem
20.6
YaRrr! The Pirate’s Guide to R
20.7
Advanced R
20.8
Advanced R Solutions
20.9
Efficient R programming
20.10
The Tidyverse Cookbook
20.11
The tidyverse style guide
20.12
Tidyverse design guide
20.13
Tidyverse Skills for Data Science
20.14
Hands-On Programming with R
20.15
The R Language
20.16
R for Graduate Students
20.17
R language for programmers
20.18
R Cookbook - 2nd edition
20.19
Cookbook for R
20.20
Tidy evaluation
20.21
Rcpp for everyone
20.22
The R Inferno
20.23
A sufficient Introduction to R
20.24
Introduction to Programming with R
20.25
Mastering Software Development in R
20.26
Introduction to R - R spatial
20.27
Another Book on Data Science : Learn R and Python in Parallel
20.28
Functional Programming in R
20.29
Advanced Object-Oriented Programming in R
20.30
Metaprogramming in R
20.31
Functional Data Structures in R
20.32
Domain-Specific Languages in R
20.33
An Introduction to R
20.34
An Introduction to Data Analysis
21
Shiny
21.1
A gRadual intRoduction to Shiny
21.2
Mastering Shiny
21.3
Mastering Shiny Solutions
21.4
Shiny Production with AWS Book
21.5
Engineering Production-Grade Shiny Apps
21.6
Supplement to Shiny in Production
21.7
Outstanding User Interfaces with Shiny
21.8
JavaScript for R
22
Social Science
22.1
Introduction to R for Social Scientists:A Tidy Programming Approach
22.2
Public Policy Analytics: Code & Context for Data Science in Government
22.3
Social Data Science with R
22.4
The Plain Person’s Guide to Plain Text Social Science
22.5
Using R for Data Analysis in Social Sciences: A Research Project-Oriented Approach
23
Sport analytics
23.1
Basketball Data Science with Applications in R
23.2
Exploring Baseball Data with R
23.3
Coding for sports analytics: get started resources
24
Statistics
24.1
Answering questions with data
24.2
Bayes rules!
24.3
A Business Analyst’s Introduction to Business Analytics: Intro to Bayesian Business Analytics in the R Ecosystem
24.4
Common statistical tests are linear models: a work through
24.5
Foundations of Statistics with R
24.6
Handbook of Regression Modeling in People Analytics
24.7
Learning statistics with R: A tutorial for psychology students and other beginners. (Version 0.6.1)
24.8
Mixed Models with R : Getting started with random effects
24.9
An Introduction to Statistical and Data Sciences via R
24.10
Statistical Rethinking
24.11
Statistical Rethinking with brms, ggplot2, and the tidyverse: Second edition
24.12
OpenIntro Statistics
24.13
Introduction to Modern Statistics
24.14
Statistical inference for data science
24.15
Statistics (The Easier Way) With R, 3rd. Ed. (TIDYVERSION)
24.16
End-to-End Solved Problems With R: a catalog of 26 examples using statistical inference
24.17
Statistics and Data with R: An Applied Approach Through Examples
24.18
TEACUPS, GIRAFFES, & STATISTICS
24.19
Modern Statistics with R
24.20
Foundations of Statistics with R
24.21
Statistical Thinking in the 21st Century
25
Time Series Analysis and Forecasting
25.1
Forecasting: Principles and Practice
25.2
Time Series Analysis and Its Applications
25.3
Hands-On Time Series Analysis with R
25.4
Practical Time Series Forecasting with R: A Hands-On Guide
25.5
Time Series - A Data Analysis Approach Using R
25.6
Applied Time Series Analysis for Fisheries and Environmental Sciences
25.7
Fisheries Catch Forecasting
26
Teaching
26.1
Data Science in a Box
26.2
rstudio4edu
26.3
Teaching Tech Together
26.4
What they forgot to teach you about teaching R
27
Text analysis
27.1
Text Mining with R
27.2
Supervised Machine Learning for Text Analysis in R
27.3
Text Mining With Tidy Data Principles
28
Version control
28.1
Happy Git and GitHub for the useR
28.2
Github actions with R
28.3
Github learning lab
28.4
The Beginner’s Guide to Git and GitHub
28.5
Git and Github for Advanced Ecological Data Analysis
29
Workflow
29.1
Agile Data Science with R
29.2
The Data Validation Cookbook
29.3
How I Use R
29.4
Github actions with R
29.5
The targets R Package User Manual
30
Other compendiums
30.1
Bookdown archive
30.2
R project book compendium
30.3
Use R! Springer series
30.4
Data Science with R: A Resource Compendium
30.5
R on the web
30.6
CRAN doc collections
Published with bookdown
Big Book of R
16
Network analysis
16.1
Network Analysis in R Cookbook
Sacha Epskamp
http://sachaepskamp.com/files/Cookbook.html